Coordination strategies for multi-robot exploration and mapping

Situational awareness in rescue operations can be provided by teams of autonomous mobile robots. Human operators are required to teleoperate the current generation of mobile robots for such applications; however, teleoperation is increasingly difficult as the number of robots is expanded. As the number of robots is increased, each robot may also interfere with one another and eventually decrease mapping performance. As presented here, through careful consideration of robot team coordination and exploration strategy, large numbers of mobile robots can be allocated to accomplish the mapping task more quickly and accurately. We present both the coordination and exploration strategies and present results from experiments in simulation as well as with up to nine mobile platforms.

Collaborative path planning for event search and exploration in mixed sensor networks

Mixed Wireless Sensor Network (WSN) is a network that consists of static and mobile sensor nodes. This article presents a collaborative framework where a team of autonomous mobile sensor nodes navigate through a sparse network with static sensors to improve the overall area coverage and search for events that may have occurred in areas not monitored by the static network. The mobile sensor nodes have limited communication and sensing ranges and collaborate to autonomously and dynamically decide their trajectories to enhance the area coverage, avoiding obstacles and collisions and adapting to new information such as failures of static nodes. In the context of the proposed framework, one can address various trade-offs. Examples include the trade off between the area coverage and the energy cost in terms of traveled distance and the one between the area coverage and information exchange among the mobile nodes. Furthermore, the proposed framework can be used to address spatially adaptive sampling. Finally, the proposed framework has been evaluated under different scenarios and has been shown to perform very well.